# RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features

arXiv: https://arxiv.org/abs/2105.11813 · HTML: https://mlsanity.com/paper/2105.11813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 5 of 5)

- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/1709.08243/) (2017)
- [Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition](https://mlsanity.com/paper/1711.05747/) (2017)
- [High-Quality, Low-Delay Music Coding in the Opus Codec](https://mlsanity.com/paper/1602.04845/) (2016)
- [A non-causal FFTNet architecture for speech enhancement](https://mlsanity.com/paper/2006.04469/) (2020)

## Built on by (most cited first, 1 of 1)

- [Empowering Global Voices: A Data-Efficient, Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesis](https://mlsanity.com/paper/2504.07858/) (2025)

## Abstract (arXiv)

Recent interest in exploiting Deep Learning techniques for Noise Suppression, has led to the creation of Hybrid Denoising Systems that combine classic Signal Processing with Deep Learning. In this paper, we concentrated our efforts on extending the RNNoise denoising system (arXiv:1709.08243) with the inclusion of complementary features during the training phase. We present a comprehensive explanation of the set-up process of a modified system and present the comparative results derived from a performance evaluation analysis, using a reference version of RNNoise as control.